{"ID":2870690,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.11499","arxiv_id":"2509.11499","title":"OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functions","abstract":"The proliferation of spectroscopic data across various scientific and engineering fields necessitates automated processing. We introduce OASIS (Omni-purpose Analysis of Spectra via Intelligent Systems), a machine learning (ML) framework for technique-independent, automated spectral analysis, encompassing denoising, baseline correction, and comprehensive peak parameter (location, intensity, FWHM) retrieval without human intervention. OASIS achieves its versatility through models trained on a strategically designed synthetic dataset incorporating features from numerous spectroscopy techniques. Critically, the development of innovative, task-specific loss functions-such as the vicinity peak response (ViPeR) for peak localization-enabled the creation of compact yet highly accurate models from this dataset, validated with experimental data from Raman, UV-vis, and fluorescence spectroscopy. OASIS demonstrates significant potential for applications including in situ experiments, high-throughput optimization, and online monitoring. This study underscores the optimization of the loss function as a key resource-efficient strategy to develop high-performance ML models.","short_abstract":"The proliferation of spectroscopic data across various scientific and engineering fields necessitates automated processing. We introduce OASIS (Omni-purpose Analysis of Spectra via Intelligent Systems), a machine learning (ML) framework for technique-independent, automated spectral analysis, encompassing denoising, bas...","url_abs":"https://arxiv.org/abs/2509.11499","url_pdf":"https://arxiv.org/pdf/2509.11499v1","authors":"[\"Chris Young\",\"Juejing Liu\",\"Marie L. Mortensen\",\"Yifu Feng\",\"Elizabeth Li\",\"Zheming Wang\",\"Xiaofeng Guo\",\"Kevin M. Rosso\",\"Xin Zhang\"]","published":"2025-09-15T01:28:51Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"physics.data-an\"]","methods":"[]","has_code":false}